From Stochastic Completion Fields to Tensor Voting
نویسندگان
چکیده
Several image processing algorithms imitate the lateral interaction of neurons in the visual striate cortex V1 to account for the correlations along contours and lines. Here we focus on two methodologies: tensor voting by Guy and Medioni, and stochastic completion fields by Mumford, Williams and Jacobs. The objective of this article is to compare these two methods and to place them into a common mathematical framework. As a consequence we obtain a sound stochastic foundation of tensor voting, a new tensor voting field, and an analytic approximation of the stochastic completion kernel.
منابع مشابه
Are Iterations and Curvature Useful for Tensor Voting?
Tensor voting is an efficient algorithm for perceptual grouping and feature extraction, particularly for contour extraction. In this paper two studies on tensor voting are presented. First the use of iterations is investigated, and second, a new method for integrating curvature information is evaluated. In opposition to other grouping methods, tensor voting claims the advantage to be non-iterat...
متن کاملTensor Completion
The purpose of this thesis is to explore the methods to solve the tensor completion problem. Inspired by the matrix completion problem, the tensor completion problem is formulated as an unconstrained nonlinear optimization problem, which finds three factors that give a low-rank approximation. Various of iterative methods, including the gradient-based methods, stochastic gradient descent method ...
متن کاملA Perceptual Grouping Approach for Visual Interpolation between Good Continuation and Minimal Path using Tensor Voting
The completion of fragmented contours is important for several processes of visual perception. However, the shape of the interpolated curves is not unique but rather varies with the completion type (amodal/modal) and contextual information. The solutions represent a balancing between the two constraints of good continuation and minimal path which is controlled by a parameter called GC-MP contra...
متن کاملA New Approach to Approximate Completion Time Distribution Function of Stochastic Pert Networks
The classical PERT approach uses the path with the largest expected duration as the critical path to estimate the probability of completing a network by a given deadline. However, in general, such a path is not the most critical path (MCP) and does not have the smallest estimate for the probability of completion time. The main idea of this paper is derived from the domination structure between ...
متن کاملNeuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion
In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...
متن کامل